{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optical Flow\n",
    "This notebook presents how to use Dali to calculate optical flow for given sequence of frames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with some handy imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os.path\n",
    "import numpy as np\n",
    "\n",
    "from nvidia.dali import pipeline_def\n",
    "import nvidia.dali.fn as fn\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting metaparameters.  \n",
    "As an example we use [Sintel trailer](https://durian.blender.org/), included in [DALI_extra](https://github.com/NVIDIA/DALI_extra) repository. Feel free to verify against your own video data.\n",
    "\n",
    "`DALI_EXTRA_PATH` environment variable should point to the place where data from [DALI extra repository](https://github.com/NVIDIA/DALI_extra) is downloaded. Please make sure that the proper release tag is checked out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 1\n",
    "sequence_length = 10\n",
    "dali_extra_path = os.environ[\"DALI_EXTRA_PATH\"]\n",
    "video_filename = (\n",
    "    dali_extra_path + \"/db/optical_flow/sintel_trailer/sintel_trailer_short.mp4\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions used for Optical flow visualization.  \n",
    "The code comes from [Tomrunia's GitHub](https://github.com/tomrunia/OpticalFlow_Visualization \"OpticalFlow_Visualization\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_colorwheel():\n",
    "    \"\"\"\n",
    "    Generates a color wheel for optical flow visualization as presented in:\n",
    "        Baker et al. \"A Database and Evaluation Methodology for Optical Flow\"\n",
    "        (ICCV, 2007)\n",
    "        URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf\n",
    "    According to the C++ source code of Daniel Scharstein\n",
    "    According to the Matlab source code of Deqing Sun\n",
    "    \"\"\"\n",
    "\n",
    "    RY = 15\n",
    "    YG = 6\n",
    "    GC = 4\n",
    "    CB = 11\n",
    "    BM = 13\n",
    "    MR = 6\n",
    "\n",
    "    ncols = RY + YG + GC + CB + BM + MR\n",
    "    colorwheel = np.zeros((ncols, 3))\n",
    "    col = 0\n",
    "\n",
    "    # RY\n",
    "    colorwheel[0:RY, 0] = 255\n",
    "    colorwheel[0:RY, 1] = np.floor(255 * np.arange(0, RY) / RY)\n",
    "    col = col + RY\n",
    "    # YG\n",
    "    colorwheel[col : col + YG, 0] = 255 - np.floor(255 * np.arange(0, YG) / YG)\n",
    "    colorwheel[col : col + YG, 1] = 255\n",
    "    col = col + YG\n",
    "    # GC\n",
    "    colorwheel[col : col + GC, 1] = 255\n",
    "    colorwheel[col : col + GC, 2] = np.floor(255 * np.arange(0, GC) / GC)\n",
    "    col = col + GC\n",
    "    # CB\n",
    "    colorwheel[col : col + CB, 1] = 255 - np.floor(255 * np.arange(CB) / CB)\n",
    "    colorwheel[col : col + CB, 2] = 255\n",
    "    col = col + CB\n",
    "    # BM\n",
    "    colorwheel[col : col + BM, 2] = 255\n",
    "    colorwheel[col : col + BM, 0] = np.floor(255 * np.arange(0, BM) / BM)\n",
    "    col = col + BM\n",
    "    # MR\n",
    "    colorwheel[col : col + MR, 2] = 255 - np.floor(255 * np.arange(MR) / MR)\n",
    "    colorwheel[col : col + MR, 0] = 255\n",
    "    return colorwheel\n",
    "\n",
    "\n",
    "def flow_compute_color(u, v, convert_to_bgr=False):\n",
    "    \"\"\"\n",
    "    Applies the flow color wheel to (possibly clipped) flow components u and v.\n",
    "    According to the C++ source code of Daniel Scharstein\n",
    "    According to the Matlab source code of Deqing Sun\n",
    "    :param u: np.ndarray, input horizontal flow\n",
    "    :param v: np.ndarray, input vertical flow\n",
    "    :param convert_to_bgr: bool, whether to change ordering and output BGR\n",
    "                                 instead of RGB\n",
    "    :return:\n",
    "    \"\"\"\n",
    "\n",
    "    flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)\n",
    "\n",
    "    colorwheel = make_colorwheel()  # shape [55x3]\n",
    "    ncols = colorwheel.shape[0]\n",
    "\n",
    "    rad = np.sqrt(np.square(u) + np.square(v))\n",
    "    a = np.arctan2(-v, -u) / np.pi\n",
    "\n",
    "    fk = (a + 1) / 2 * (ncols - 1)\n",
    "    k0 = np.floor(fk).astype(np.int32)\n",
    "    k1 = k0 + 1\n",
    "    k1[k1 == ncols] = 0\n",
    "    f = fk - k0\n",
    "\n",
    "    for i in range(colorwheel.shape[1]):\n",
    "        tmp = colorwheel[:, i]\n",
    "        col0 = tmp[k0] / 255.0\n",
    "        col1 = tmp[k1] / 255.0\n",
    "        col = (1 - f) * col0 + f * col1\n",
    "\n",
    "        idx = rad <= 1\n",
    "        col[idx] = 1 - rad[idx] * (1 - col[idx])\n",
    "        col[~idx] = col[~idx] * 0.75  # out of range?\n",
    "\n",
    "        # Note the 2-i => BGR instead of RGB\n",
    "        ch_idx = 2 - i if convert_to_bgr else i\n",
    "        flow_image[:, :, ch_idx] = np.floor(255 * col)\n",
    "\n",
    "    return flow_image\n",
    "\n",
    "\n",
    "def flow_to_color(flow_uv, clip_flow=None, convert_to_bgr=False):\n",
    "    \"\"\"\n",
    "    Expects a two dimensional flow image of shape [H,W,2]\n",
    "    According to the C++ source code of Daniel Scharstein\n",
    "    According to the Matlab source code of Deqing Sun\n",
    "    :param flow_uv: np.ndarray of shape [H,W,2]\n",
    "    :param clip_flow: float, maximum clipping value for flow\n",
    "    :return:\n",
    "    \"\"\"\n",
    "\n",
    "    assert flow_uv.ndim == 3, \"input flow must have three dimensions\"\n",
    "    assert flow_uv.shape[2] == 2, \"input flow must have shape [H,W,2]\"\n",
    "\n",
    "    if clip_flow is not None:\n",
    "        flow_uv = np.clip(flow_uv, 0, clip_flow)\n",
    "\n",
    "    u = flow_uv[:, :, 0]\n",
    "    v = flow_uv[:, :, 1]\n",
    "\n",
    "    rad = np.sqrt(np.square(u) + np.square(v))\n",
    "    rad_max = np.max(rad)\n",
    "\n",
    "    epsilon = 1e-5\n",
    "    u = u / (rad_max + epsilon)\n",
    "    v = v / (rad_max + epsilon)\n",
    "\n",
    "    return flow_compute_color(u, v, convert_to_bgr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Dali\n",
    "### Define the Pipeline.\n",
    "The pipeline below loads video files and computes optical flow for the sequence of frames.\n",
    "For more information, please refer to [readers.video](../../operations/nvidia.dali.fn.readers.video.html) and [optical_flow](../../operations/nvidia.dali.fn.optical_flow.html) documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "@pipeline_def\n",
    "def optical_flow_pipe():\n",
    "    video = fn.readers.video(\n",
    "        device=\"gpu\", filenames=video_filename, sequence_length=sequence_length\n",
    "    )\n",
    "    of = fn.optical_flow(video, output_grid=4)\n",
    "    return of"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build and Run DALI Pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9, 180, 320, 2)\n"
     ]
    }
   ],
   "source": [
    "pipe = optical_flow_pipe(batch_size=batch_size, num_threads=1, device_id=0)\n",
    "pipe.build()\n",
    "pipe_out = pipe.run()\n",
    "flow_vector = np.array(pipe_out[0][0].as_cpu())\n",
    "print(flow_vector.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above you can see the shape of calculated `flow_vector` (in NFHWC format). It contains 2 channels: flow vector in `x` axis and flow vector in `y` axis. Output resolution is determined by `output_grid` option passed to `optical_flow` operator: for `output_grid = 4`, 4x4 grid is used for flow calculation, thus resolution in every dimension being 4 times smaller, than resolution of the input image.\n",
    "\n",
    "### Visualize Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f88f80f7790>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "of_result = flow_to_color(flow_vector[sequence_length // 2])\n",
    "plt.imshow(of_result)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
